Enrollment factors and bias of disease prevalence estimates in administrative claims data

• Published in: Annals of epidemiology 2015-07, Vol.25 (7), p.519-525.e2
• Main Authors: Jensen, Elizabeth T., MPH, PhD, Cook, Suzanne F., PhD, Allen, Jeffery K., MS, Logie, John, PhD, Brookhart, Maurice Alan, PhD, Kappelman, Michael D., MD, MPH, Dellon, Evan S., MD, MPH
• Format: Article
• Language: English
• Subjects: Administrative claims data; Adolescent; Adult; Bias; Child; Child, Preschool; Cross-Sectional Studies; Female; Gastrointestinal Diseases - epidemiology; Health Services - statistics & numerical data; Humans; Infant; Infant, Newborn; Insurance Claim Review - statistics & numerical data; Insurance, Health - statistics & numerical data; Insurance, Pharmaceutical Services - statistics & numerical data; Internal Medicine; Male; Middle Aged; Patient Selection; Prevalence; Selection bias; United States - epidemiology; Young Adult
• ISSN: 1047-2797; 1873-2585
• DOI: 10.1016/j.annepidem.2015.03.008

Abstract Purpose Considerations for using administrative claims data in research have not been well-described. To increase awareness of how enrollment factors and insurance benefit use may contribute to prevalence estimates, we evaluated how differences in operational definitions of the cohort impact observed estimates. Methods We conducted a cross-sectional study estimating the prevalence of five gastrointestinal conditions using MarketScan claims data for 73.1 million enrollees. We extracted data obtained from 2009 to 2012 to identify cohorts meeting various enrollment, prescription drug benefit, or health care utilization characteristics. Next, we identified patients meeting the case definition for each of the diseases of interest. We compared the estimates obtained to evaluate the influence of enrollment period, drug benefit, and insurance usage. Results As the criteria for inclusion in the cohort became increasingly restrictive the estimated prevalence increased, as much as 45% to 77% depending on the disease condition and the definition for inclusion. Requiring use of the insurance benefit and a longer period of enrollment had the greatest influence on the estimates observed. Conclusions Individuals meeting case definition were more likely to meet the more stringent definition for inclusion in the study cohort. This may be considered a form of selection bias, where overly restrictive inclusion criteria definitions may result in selection of a source population that may no longer represent the population from which cases arose.